Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Jurnal ULTIMATICS Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Linguistik Komputasional Specta Journal of Technology
Agus Zainal Arifin
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 28 Documents
Search

 1   2   3 
INDONESIAN-TRANSLATED HADITH CONTENT WEIGHTING IN PSEUDO-RELEVANCE FEEDBACK QUERY EXPANSION Ivanda Zevi Amalia; Akbar Noto Ponco Bimantoro; Agus Zainal Arifin; Maryamah Faisol; Rarasmaya Indraswari; Riska Wakhidatus Sholikah
Jurnal Ilmiah Kursor Vol 11 No 1 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i1.249

Abstract

In general, hadith consists of isnad and matan (content). Matan can be separated into several components for example a story, main content, and some additional information. Other texts besides main content, such as isnad and story can interfere the retrieval process of relevant documents because most users typically use simple queries. Thus, in this paper, we proposed a Named Entity Recognition (NER) component weighting model in improving the Indonesian hadith retrieval system. We did 3 test scenarios, the first scenario (S1) did not separate the hadith into several components, the second scenario (S2) separated the hadith into 2 components, isnad and matan, and the third scenario separated the hadith into 4 components, isnad, background story, content, and additional information. From the experimental results, it is found that the TF-IDF with rocchio algorithm in query expansion outperforms DocVec. Also, separation and weighting of the hadith components affect the retrieval performance because isnad can be considered as noise in a query. Separation of 2 separate components had the best overall results in general although 4 separate components showed better results in some cases with precision up to 100% and 70% recall.
Pembobotan Berdasarkan Tingkat Kesamaan Semantik pada Metode Fuzzy Semi-Supervised Co-Clustering untuk Pengelompokkan Dokumen Teks Galang Amanda Dwi P.; Gregorius Edwadr; Agus Zainal Arifin
Ultimatics : Jurnal Teknik Informatika Vol 6 No 2 (2014): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (393.541 KB) | DOI: 10.31937/ti.v6i2.333

Abstract

Nowadays, a large number of information can not be reached by the reader because of the misclassification of text-based documents. The misclassified data can also make the readers obtain the wrong information. The method which is proposed by this paper is aiming to classify the documents into the correct group. Each document will have a membership value in several different classes. The method will be used to find the degree of similarity between the two documents is the semantic similarity. In fact, there is no document that doesn’t have a relationship with the other but their relationship might be close to 0. This method calculates the similarity between two documents by taking into account the level of similarity of words and their synonyms. After all inter-document similarity values obtained, a matrix will be created. The matrix is then used as a semi-supervised factor. The output of this method is the value of the membership of each document, which must be one of the greatest membership value for each document which indicates where the documents are grouped. Classification result computed by the method shows a good value which is 90 %. Index Terms - Fuzzy co-clustering, Heuristic, Semantica Similiarity, Semi-supervised learning.
Peringkasan Otomatis Multi Dokumen menggunakan Hirarki Kluster Lukman Hakim; Fadli Husein Wattiheluw; Agus Zainal Arifin; Aminul Wahib
Jurnal Linguistik Komputasional Vol 1 No 2 (2018): Vol. 1, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v1i2.86

Abstract

Multi-document summarization is a technique for getting information. The information consists of several lines of sentences that aim to describe the contents of the entire document relevantly. Several algorithms with various criteria have been carried out. In general, these criteria are the preprocessing, cluster, and representative sentence selection to produce summaries that have high relevance. In some conditions, the cluster stage is one of the important stages to produce summarization. Existing research cannot determine the number of clusters to be formed. Therefore, we propose clustering techniques using cluster hierarchy. This technique measures the similarity between sentences using cosine similarity. These sentences are clustered based on their similarity values. Clusters that have the highest level of similarity with other clusters will be merged into one cluster. This merger process will continue until one cluster remains. Experimental results on the 2004 Document Understanding Document (DUC) dataset and using two scenarios that use 132, 135, 137 and 140 clusters resulting in fluctuating values. The smaller the number of clusters does not guarantee an increase in the value of ROUGE-1. The method proposed using the same number of clusters has a lower ROUGE-1 value than the previous method. This is because in cluster 140 the similarity values ​​in each cluster experienced a decrease in similarity values.
Segmentasi Otomatis pada citra Cone Beam Computed Tomography Gigi didasari Metode Level Set dengan Operasi dan Polinomial Fitting Fahmi Syuhada Syuhada; Agus Zainal Arifin
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 4 No 1 (2020): June 2020
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.062 KB) | DOI: 10.29303/jcosine.v4i1.321

Abstract

Abstract Automatic Segmentation of dental cone beam computed tomography (CBCT) images is challenging due to the intensity of the teeth that have low level intensity. In this paper we proposes a new method for automatic teeth segmentation in slices of CBCT images based on level let method using morphology operators and polynomial fitting. Morphology operators are used to construct the Region of Interest (ROI) area of dental objects in the image slice. ROI is used to focus the analysis process on areas of dental objects which generally have a polynomial pattern distribution. Polynomial fitting is obtained to estimation arc of teeth structure in CBCT images. Level Set is implemented to evolve the ROI to obtain the contours of dental objects. Comparison between proposed method result and the ground truth images shows that the method gives best average accuracy, sensitivity, and specificity value of 99.02%, 95.32%, 99.09%, respectively. This value that the proposed method is promising for accurate segmentation of the entire tooth form on CBCT images.
Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K – Harmonic Means Tegar Palyus Fiqar; Saiful Bahri Musa; Fitrah Maharani Humaira; I Made Widiartha; Darlis Herumurti; Agus Zainal Arifin
SPECTA Journal of Technology Vol. 2 No. 3 (2018): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.517 KB) | DOI: 10.35718/specta.v2i3.3

Abstract

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.
PENGGUNAAN ANALISA FAKTOR UNTUK KLASIFIKASI CITRA PENGINDERAAN JAUH MULTISPEKTRAL Agus Zainal Arifin; Wiwik Dyah Septiana Kurniati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 1, No 1 Juli 2002
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (421.672 KB) | DOI: 10.12962/j24068535.v1i1.a91

Abstract

Proses clustering bisa berlangsung baik secara hierarchical (split dan merge) maupun partitional (partisi). Proses split yang pembagiannya berdasarkan histogram lebih mudah dilakukan pada satu dimensi, sehingga dibutuhkan proses transformasi. Metode transformasi yang umum digunakan adalah Principal Component Analysis (PCA). Namun PCA ternyata hanya didasarkan pada pencarian dimensi bervariansi maksimum, sehingga memungkinkan terjadinya overlapping kelas, dalam arti ada kelas yang tidak dapat dipisahkan Pada penelitian ini, metode transformasi yang digunakan adalah Analisa Faktor (Factor Analysis / Canonical Analysis). Metode ini lebih baik bila dibandingkan dengan metode Principal Component Analysis (PCA). Sebab, Analisa Faktor mentransformasi sekaligus memilah cluster dalam feature space. Tiga proses utama dalam penelitian ini yaitu split, merge, dan partitional K-means clustering. Citra multispektral ditransformasi menjadi satu dimensi. Histogram satu dimensi displit dengan pemilihan puncak kurva. Merge menggabungkan cluster hasil split tersebut. Cluster yang berdekatan digabungkan menjadi cluster baru. K-means clustering digunakan untuk mendeteksi lokasi pusat cluster (prototipe cluster) dan sekaligus mengelompokkan pixel ke setiap cluster. Hasil penelitian ini dibandingkan dengan hasil algoritma clustering yang proses transformasinya menggunakan PCA. Hasil perbandingan membuktikan bahwa clustering yang proses transformasinya menggunakan Analisa Faktor menghasilkan heterogenitas antar cluster lebih tinggi (Tr(SB) meningkat antara 0.83 % sampai 19.58 %). Adapun kekompakan tiap cluster tidak selalu optimal. Hal ini sangat mungkin disebabkan jumlah kelas sampel kurang banyak dan pengambilan sampel di tiap kelas kurang bervariasi. Kata kunci: Analisa Faktor, complete link, K-means clustering, Scatter within class, Scatter between class
KOMPRESI CITRA PENGINDERAAN JAUH MULTISPEKTRAL BERBASIS CLUSTERING DAN REDUKSI SPEKTRAL Agus Zainal Arifin; Nova Hadi Lestriandoko
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 2, No 1 Januari 2003
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.187 KB) | DOI: 10.12962/j24068535.v2i1.a110

Abstract

Kompresi Citra pada aplikasi Penginderaan Jauh Multispektral merupakan kebutuhan yang sangat vital, sebab citra multispektral merupakan citra yang membutuhkan ruang penyimpanan yang sangat besar. Di sisi lain, citra multispektral memiliki karakteristik istimewa yang dapat dimanfaatkan untuk meningkatkan efektifitas proses kompresinya. Karakteristik ini berkaitan dengan penggunaan citra tersebut dalam proses klasifikasinya. Dengan demikian, kompresi citra multispektral tidak perlu menggunakan cara konvensional, melainkan dengan memanfaatkan karakteristik yang dimilikinya. Penelitian ini membahas sebuah metode kompresi citra multispektral yang mengintegrasikan metode clustering, manipulasi spektral, serta pengkodean. Metode clustering yang digunakan adalah Improved Split and Merge Clustering (ISMC). Pada proses manipulasi spektral digunakan Principal Component Analysis (PCA). Sedangkan untuk pengkodean digunakan metode kompresi data lossless yaitu metode Lempel-Ziv Welch (LZW). Integrasi dari clustering, manipulasi spektral, dan pengkodean ini dibagi menjadi 2 kombinasi, yakni clustering-LZW dan PCA-clustering-LZW. Evaluasi dilakukan dengan mengukur rasio kompresi, waktu komputasi, jumlah cluster, dan ukuran kesalahan yang meliputi total error, maksimum error, dan rata-rata error. Dari hasil uji coba, didapatkan bahwa masing-masing metode ini memiliki keunggulan yang berbeda pada tiap faktor evaluasinya, sehingga pengguna dapat memilih untuk menggunakan metode yang tepat sesuai kebutuhannya. Kata kunci : Clustering, kompresi citra, citra multispektral, ISMC, reduksi spektral, LZW
ANALISA KERAPATAN TRABECULAR BONE BERBASIS GRAPH BERBOBOT PADA CITRA PANORAMA GIGI UNTUK IDENTIFIKASI OSTEOPOROSIS Zainal Abidin; Agus Zainal Arifin
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 2, Juli 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.774 KB) | DOI: 10.12962/j24068535.v7i2.a174

Abstract

Osteoporosis is bone disease indocated by low bone mass density and micro architectures disorder which lead to bone fragility or fractures. Graph may be useful to describe density of trabeculae bone due to morphological change on mandibular bone in dental panoramic radiographs. The density of trabecular bone can be discribed by generating graph. Trabecular image firstly was transformed to binary image. A white pixel on the binary image presented as part of trabeculae, which assumed as an isolated node on the graph. Graph generation by Erdos and Royi method was used to build connections between an isolated node and others. This paper introduced the use of weight on each node based on probabilities average of its neigbourhoods. Graph’s properties which used to measure the density were degree and cluster coefficient. Both of properties are used to build feature space. Feature space indicated distribution of node on dense or sparse area. Early indication of osteoporosis could be assumed that ratio of nodes on dense area were greater than that on sparse area. We achieved accuration of 54%, sensitivity of 60%, and spesificity of 49%.   Keywords: osteoporosis, trabeculae, random graph, graph berbobot
Co-Authors Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ahmad Reza Musthafa Akbar Noto Ponco Bimantoro Alhaji Sheku Sankoh Ali Fauzi Alif Akbar Fitrawan Aminul Wahib Andi Baso Kaswar Andi Baso Kaswar Anny Yuniarti Ari Effendi Arif Fadllullah Arya Yudhi Wijaya Aryo Harto Aryo Harto Biandina Meidyani Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Darlis Herumurti Diana Purwitasari Dini Adni Navastara Dyah S. Rahayu Evy Kamilah Ratnasari Fadli Husein Wattiheluw Fahmi Syuhada Syuhada Fajrin, Ahmad Miftah FATRA NONGGALA PUTRA Fitrah Maharani Humaira Galang Amanda Dwi P. Go Frendi Gunawan Gregorius Edwadr Gulpi Qorik Oktagalu Pratamasunu I Made Widiartha Ivanda Zevi Amalia Jayanti Yusmah Sari Khadijah F. Hayati Khadijah Fahmi Hayati Holle Khoirul Umam Lailly Qolby Lailly S. Qolby Luh Putu Ratna Sundari Lukman Hakim Lukman Hakim Mamluatul Hani’ah Maryamah Faisol Maryamah Maryamah Muhammad Imron Rosadi Munjiah Nur Saadah Mutmainnah Muchtar Naser Jawas Nova Hadi Lestriandoko Pasnur Pasnur Putu Praba Santika Randy Cahya Wihandika Rarasmaya Indraswari Ratri Enggar Pawening Resti Ludviani Rigga Widar Atmagi Riska Wakhidatus Sholikah Saiful Bahri Musa Saiful Bahri Musa Saprina Mamase Siti Mutrofin Sonny Christiano Gosaria Subali, Made Agus Putra Tegar Palyus Fiqar Wincoko Putro, Fidi Wiwik Dyah Septiana Kurniati Yuita Arum Sari Zainal Abidin